k. davary

Limited fresh water resources and access to these resources as well as providing food security for the growing world population have led researchers to make extensive efforts in the field of optimal management of water consumption and determining the cultivation pattern in different regions. Therefore, identifying cultivated crops in a region and determining their area can be very effective in land management and water allocation in these regions. With the growth and advancement of technology in the field of satellite and remote sensing in recent decades, the use of satellite images in order to identify types of land use and types of cultivated products has expanded greatly. Sentinel-1 (radar) and Sentinel-2 (multi-spectral) satellites have been very popular in agriculture due to their improved spatial resolution (10 meters) and appropriate time resolution (5 days for Sentinel 2 and 12 days for Sentinel 1).

The studied area is located downstream of the Fariman dam in an area of 22.51 square kilometers (5122 hectares) and the central coordinates are 35 degrees 41 minutes and 59 seconds north latitude and 59 degrees 50 minutes and 49 seconds east longitude. In order to classify satellite images and produce crop maps, ground observation data is needed to train the classification model and also evaluate the accuracy of the results. For this purpose, sample points were taken from different land uses in the region, using GPS. Since it was not possible to take enough samples for all land uses and crops in the determined border, a larger sampling area was selected. Then, all collected data were sorted and for each class, 70% of the data was randomly used to train the classification model and 30% was used to validate the obtained classification results. In the present study, Sentinel 2 satellite images for the first 6 months (crop season) of 2021 and 2022 and digital elevation image (DEM) of the study area were considered. According to the surveys conducted and the reports of the agricultural jihad of Fariman city, the main crops cultivated in the region include maize, tomato, sugar beet, wheat and barley. Therefore, according to the phenological stages of these products in the region, the appropriate time series of images was selected. The accuracy of the classified map was evaluated using the Kappa coefficient and overall accuracy.

In order to identify and separate the land use in the study area according to the major cultivated crops, first the agricultural calendar of the crops was determined. Then, satellite images were selected based on crop cultivation period. Based on the evaluation indexes of commission error, omission error, overall accuracy as well as the Kappa coefficient, it was observed that the identification of classes and land use was done well and with high accuracy, so that the overall accuracy for the classification map of 2022 is equal to 0.97 and the kappa coefficient value was 0.94. In order to compare land use changes during the two years 2022 and 2021, classification was also done for the images of the crop year 2021. Since the training samples of agricultural crops were not available separately and in sufficient numbers in the crop year of 2021, the classification map of this year was produced only based on the type of land use, and all crops in one class entered the classification model training process. The values of overall accuracy and kappa coefficient in 2021 were obtained as 0.97 and 0.95 respectively. According to the obtained results, the area of the orchard class has increased since 2021 compared to 2022. After repeated field visits to the study area and investigation of some land uses that had been changed and turned into orchard use, it was found that in some areas in 2022 there was the growth of villa gardens and in some areas the farmers have converted cropland to orchard (construction of an orchard). Even in some cases, the old orchard in the region was destroyed by the farmers and the land was fallow for 2 to 3 years (2021, fallow). In 2022, the farmer built a new orchard. It is also necessary to mention that fallow lands are included in the soil class depending on whether they are newly plowed or have no vegetation, and if weeds have grown on these lands, they are included in the rangeland class. 

The effective management of water resources from dams for agricultural purposes necessitates the identification of land use downstream of the dams, along with determining the types of crops and their respective areas. In this study, Sentinel 2 satellite images were employed to classify and delineate land use associated with agricultural cultivation downstream of the Fariman dam in Razavi Khorasan Province, spanning the crop years of 2021 and 2022. The results indicate that the Sentinel 2 satellite demonstrates a high capacity to differentiate between various types of land use and crops. The generated map depicting changes in land use and crop cultivation areas can be instrumental in water use planning and the allocation of water resources.

Adaptation to water scarcity is a topic that has received more attention recently by the Ministry of Energy as well as water experts. Providing appropriate solutions and long-term and efficient plans for this issue requires discussion and consensus on the subject. One of the issues that causes consensus to fail in various scientific disciplines is misunderstanding of concepts and la ack of common technical reference. To come up with appropriate solutions to water scarcity, it is necessary to understand both “adaptation” and “Scarcity”. There are various meanings associated with these two terms that need to be examined in relation to each other and avoid the use of them interchangeably by providing the correct definition of these concepts. This article discusses some of these concepts.

In the present study, a combination of delayed dams in the upstream basin with the coastal dike in the downstream of residential area of the Kan river was studied, so that for this combination, the cost of constructing the structures is minimized and the reduction of flood peak is maximized. For this purpose, multi-criteria decision method and TOPSIS method were used and the best option was determined. This research showed that for the best combination, if 3 delayed dams with coastal river dike are constructed, the peak of output flood reduce to 31.5%. In addition, the diffusion and elongation of the output hydrograph in the superior state were significant and the time of concentration of flood increase.

Runoff is a main component of water balance in the watershed scale which its accurate estimation helps to better hydrological simulation of watershed. In this study, the SWAT (Soil Water Assessment Tool) model was used to simulate runoff in Neyshabour watershed with area of 9500 km2 for the 2007 to 2010 period. To this means, SWAT model was calibrated based on measured runoff data at three hydrometric stations (Andarab, Kharve Majmoo and Hossein Abad) during 2000-2007. Calibration, uncertainty and sensitivity analysis were done using SUFI-2 algorithm. According to the results, parameters such as fraction of transmission losses, moisture condition curve number and factors related to snowmelt showed the greatest sensitivity to run off estimation. The model calibration in all three stations; Andarab, Kharve Majmoo and Hossein Abad, was evaluated as appropriate by referring the values of 0.84, 0.77 and 0.92 for the Nash-Sutcliff efficiency coefficient, respectively, although underestimations for the peak amounts of hydrographs were observed relatively. The values of the mentioned coefficient were obtained as 0.92, 0.66 and 0.71 for the validation period at the stations of Andarab, Kharve Majmoo and Hossein Abad, respectively. Also root mean square error values varied between 0.46 to 0.57 m3/s and 0.06 to 0.19 m3/s in the same three hydrometric stations for the calibration and validation periods, respectively. The results showed that considering the crop management concepts significantly improved runoff estimation.

In many climate studies, land surface models are forced by meteorological data, and there is no attempt to check the quality of the data. The purpose of this study is to evaluation of GLDAS1 meteorological forcing data and application of bias correction method for these data. The weather data corrected will be input into the Noah-MP model in the next study.
Observations for the Neyshaboor Basin have been collected at a temporal resolution of one day during the period 2000–2009 for ten years from fourteen stations and evaporation stations and a meteorological station in the basin. The variables of temperature, relative humidity, short solar wavelength, and air pressure, with correlation coefficients of 0.94, 0.77, 0.74 and 0.6, respectively, were in good agreement with the observations. The GLDAS1 precipitation data for most of this basin is less than observations. The precipitation has been corrected by matching the mean and the coefficient of variation of GLDAS data with observational data. The results of applying the correction method were satisfactory, so that difference in average daily precipitate between GLDAS1 data and observational data was effectively reduced to 0.048 mm. Also, the correlation between the number of wet days (precipitation over 0.3 mm) in the observational data with the corrected data compared to the uncorrected data improved from -1.17 to 0.93 and the first-order autocorrelation in the observational data with corrected data has improved better than uncorrected data and has grown from -2.4 to 0.41. The corrections were the highest during January, February, October, November and December. the bias correction applied seems to correct the precipitation well during May to September.

Using normal Kriging in the evaluation of groundwater quality can not specify the areas susceptible to pollution. In this study, to determine the extent of exceeded nitrate pollution (50 mg/L) as well as identifying the areas susceptible to pollution, nitrate data of 287 wells within an area of 320 square kilometers in Mashhad and the statistical range of 2002 to 2011 was analyzed by R software. The process of evaluation and analysis included re-arranging data sheets, detection of outliers, removing the trend, studying the isotropy, determination of the optimum variogram models for each year, and formulating the spatial and temporal Indicator Kriging. The results showed that Matern semivariogram was the best model for explaining the spatial structure data in each year. Also, in the statistical range respectively 7.1, 5.8, 7.2, 8.5, 8.6, 9.4, 10.4, 2.1, and 2.4 percent of the studied area has passed the pollution allowed limit. It seems that more polluted areas have been controlled since 200 by some strategies such as shutting down the polluted wells, preventing new well digging, transferring water from Doosti Dam, using appropriate agricultural wells for drinking section and implementation of wastewater collection network. In addition, respectively 10.4, 11.7, 12.4, 9.4, 14.3, . , 6, 11.6 and 11.5 percentage of total area was prone to pollution during these years. Therefore, these areas need more attention for monitoring and controlling the source of pollution.

Soil moisture retention curve (SMRC) is an important soil property which expresses reaction between matric potential and moisture of soil. Direct measurement of soil matric potential and moisture is labour- and time-consuming. In order to prevail this problem, indirect methods are used for SMRC prediction. Pedotransfer functions (PTFs) are one of these indirect methods. This study was carried out to evaluate three internal pedotransfer functions, first and second models of Ghorbani and Homaee (1381) and Sepaskhah and Bondar (2002) derived in Iran, to predict SMRC in some Iranian soils. Also we tried to develop new different PTFs with better performance using the available information. Therefore 42 soil samples with spatial distribution from northern region of Iran, Amol, Babol and Karaj were selected and divided in Loam (20 samples) and Clay Loam (22 samples) texture classes. In evaluation of all existing PTFs, all 42 soil samples, and in developing new PTFs, 36 soil samples were used. The remaining six samples (three samples in each texture class) were used for validation of the new developed PTFs. In evaluation of the existing PTFs, results showed that the first and second models of Ghorbani and Homaee had alike and appropriate prediction of moisture in whole range of matric potential, whereas Sepaskhah and Bondar did not show an appropriate prediction. By the way, none of these PTFS had noticeable preference in specific texture classes in comparison with the others. New developed PTFS were highly significant (p)

This experiment was conducted at research greenhouse of college of agriculture, Ferdowsi University of Mashhad in 1381-1382. The experiment was designed based on the splitted plots in the form of completely randomized design and in four replications. Irrigation intervals were in three levels of 12, 4, and 2 times per day at primary plots, and three substrates of new perlite, used perlit, and rice bran at secondary plots. We used Paris Island as the lettuce seed. Wet weight, dry weight, and height were influenced from irrigation interval. Accordingly which 4- and 12-day irrigation intervals resulted in 466.39 and 386.94 g corresponding to highest and lowest dry weight, respectively and 12-day irrigation interval arose increase in lettuce height. Significant differences for wet and dry weights were found under different substrates. High wet and dry weights were due to used perlit and rice bran substrates, respectively. There were no significant interactions between irrigation interval and substrate on all of the growth properties of lettuce.

In recent years, many researchers have attempted to estimate the soil hydraulic functions (e.g. soil moisture characteristics curve, and hydraulic conductivity function) using particle-size distribution (PSD) curve. In these studies, an accurate mathematical representation of PSD is required for fitting the observed data. So far, some mathematical models were developed with different limitations. The goodness of fit is directly related to the number of the model parameters. However, estimating the parameters for higher-parameter models which have no mathematical or physical significance is a problem. Among the current models, 2-parameter Log-normal distribution model with mathematical significant parameters has been considered as a basis for many studies. In this study, it is indicated that the 2-parameter Log-normal distribution model can not be very accurate for representation of the PSD for all of soil textural classes. As an alternative, 2-parameter Gamma distribution model is proposed for more accurate representation of the PSD that its two parameters also are mathematical significant and readily computable. These two models have been compared in fitting the observed PSD data of 461 soil samples from UNSODA soil database. Gamma distribution model indicated a pronounced improvement in representation of the PSD. Based on Coefficient of determination (R2), in 362 samples and based on RMSE, in 323 samples, Gamma distribution model showed a better representation of the PSD than Log-normal. To evaluate the significance of the difference between two models, a t-test was performed. The results showed that, at confidence level of 1%, the R2-values of the Gamma model are significantly greater than those of Log-normal model. Also, at confidence level of 5%, a significant difference between the RMSE-values of two models was shown. Therefore, 2-parameter Gamma distribution model is judged to be better than 2-parameter Log-normal model for representation of PSD.

Sediment load-discharge data of hydrometric stations in the north of Great Khorasan province were studied. Twenty nine stations were selected and the mean annual sediment yield was computed using sediment rating curves. The total annual sediment yield for these catchments (61.5 to 16800 km2) varied between 4.8 to 19500 M ton/year and the specific sediment yield varied between 62 to about 4000 ton/year/km2. Due to large variations in the total and specific sediment yield, 29 selected catchments were divided in two groups: 17 large catchments (area>500 km2) and 12 smaller ones (area)

Deficit irrigation is a method to promote water use efficiency (WUE) in a farm under water shortage conditions, however, the consequences is that yield per area decreases. To determine production functions for three cotton cultivars, an experiment was conducted during 1381 growing season on a silty clay loam soil in HashemAbad Agricultural Research Station in Gorgan. This study was performed using a split-plot design with 3 replications on three cotton cultivars. A line-source sprinkler irrigation system was used with 54 plots in each side of the line (3cultivars* 6treatments* 3replications). To estimate root zone water deficit, climatic data and cotton crop coefficients during the growing season were used. For each cultivar second order equations were derived to relate yield and applied water. However, linear relationships were established to relate yield and evapotranspiration. In addition, based on Doorenbos and Kassam formula yield response factors were found to be 1.02, 0.96 and 1.01 for Sahel, Say Ocra, and 818-312 cultivars, respectively. Such yield response factors can be used to optimize irrigation planning under water shortage conditions.

This research is aimed to predict erosion and sedimentation of Tang-e-Kenesht basin in Kermanshah province using MPSIAC and EPM models in GIS software. This basin has about 14348 hectare area. This region has various vegetation, geology and soil texture and land use types. The basin has divided into 9 sub-basins and its maximum and minimum elevations are 3300 and 1400 m, respectively. Needed data were collected in part through published reports, while the remainings were derived by field survey. Necessary maps in MPSIAC and EPM models were prepared in Autocad-2000 medium and were transported to Arc-Info, after some revisions to them. After constructing topologies for all polygons, we entered all layers weights in Arc-View software. Combinations of all layers were managed thereafter. Nine layers for MPSIAC model and three layers for EPM model were combined to result the final layer of erosion and sedimentation. Basin erosion was calculated as 1002.7 and 1739.2 m3/Km2 by MPSIAC and EPM models, respectively. The result for basin sediment was 521.7 and 307.8 m3/Km2, respectively. Thereafter, medium and high erosion classes were found for the two models under study, respectively. Due to not fully compatible tables for EPM model and its subjective nature, one can conclude that MPSIAC model may have better performance.